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How Will Gaming Apps Deal With SKAdNetwork 4.0?

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How Will Gaming Apps Deal With SKAdNetwork 4.0?

The introduction of the SKAdNetwork in 2018, and the ATT rollout in 2020, means app marketers have had to change how they measure their app's performance. But now, with a new update on the horizon, how will things change?

We spoke with Alexey Gusev, Lead Performance Marketing at Goodgame Studios, as part of our Apptivate podcast, to understand how his team adapted its performance measurement strategies with the SKAdNetwork for every game, regardless of genre. We also talked about how SKAdNetwork 4.0 could change its mobile game strategies.

What is SKAdNetwork again?

The SKAdNetwork framework, also known as SKAN, is a user-privacy-preserving approach that provides validation of advertiser-driven app installations in combination with campaign and attribution measurement capabilities. The framework fully aligns with Apple's latest data safety and security regulations and is compatible with all iOS 14.5 devices or higher. The SkAdNetwork API forwards postbacks upon installs or reinstalls.

Back in 2018, advertisers were given IDFA access as a default. While there was an option to opt-out of ad tracking, most users didn't and so SkAdNetwork was not widely adopted within the mobile industry. But with the introduction of the ATT (App Tracking Transparency) framework in 2020, the game shifted. Advertisers could now only obtain IDFA data for users who had opted-in to ad tracking for their app. ID traffic has since dropped and more developers have started using SkAdNetwork, which has become an attractive solution as it allows for install attribution measurement without user consent.

Things are set to change again with the 4.0 rollout. The new update makes the API more usable, however, these updates won’t be live until Autumn 2022 (more on that later).

For now, let’s focus on how Goodgame Studios has adapted ad campaign measurement for SKAdNetwork and how they reacted to the ATT rollout back in 2020.

« When the ATT rolled out we decided to double-down on SKAN and tried to be ahead of the market »
Alexey Gusev, Lead Performance Marketing, Goodgame Studios

Goodgame Studios: Their reaction to SKAN and the ATT rollout

Goodgame Studios is a mobile game publisher and developer and part of The StillFront Group. Alexey works within the in-house marketing agency in a team of five, managing up to eight different gaming titles.

Goodgame's portfolio is varied and includes casual/hyper-casual genres to hardcore titles — but their mobile marketing and measurement approaches are very different depending on the game genre.

As most app marketers would agree, launching a new game is always exciting, and analyzing those first KPIs is a huge milestone in any game launch — how successful is it looking? Are users loving playing it? When Goodgame Studios launched their game BitLife, for example, they already had the game setup in multiple languages, so they had a rough idea of KPI expectations, and it monetized quickly.

With changes to the IDFA and ATT rollout over the last year and a half, every one of their gaming apps had to reprogram itself to track and optimize campaigns in a world where IDs are less accessible — which can be challenging. The team had to learn about SkAdNetwork and how to leverage it across different titles.

“What we learned during this whole process is that every title, every game–not even every genre–every game needs to be approached very differently.” — Alexey Gusev

ATT has affected Goodgame Studios differently across different titles

Goodgame Studios found that iOS is most profitable for hardcore titles with a long monetization cycle. But finding a long-term solution was a steep learning curve for the team.

"When the ATT rolled out we decided to double-down on SKAN and tried to be ahead of the market and work with the solution we'd have to stick with in the future." — Alexey Gusev

For every hardcore game, the team had to understand what conversion values they wanted to keep, which events they'd like to see, which revenue brackets they didn't want to lose, the refresh interval they'd like to adopt, and much more.

After a lot of experimentation, the team found an optimal solution. They adopted a seven-day optimization and combined the timers to give a rough idea about retention values and paired that with monetisation.

"We essentially had something that pinged when the user was playing, or logged into the game every several hours, in order to prolong this 24-hour activity timer and then record as many purchases as possible." — Alexey Gusev

This is a brand new world for technical developers and the Goodgame team experienced some communication obstacles — but overall, they were satisfied with the results.

There is no uniform way to measure with SKAdNetwork

It's important to approach every title and every game differently — they all require their own measurement strategy. App marketers require a deep understanding of what's going with the events, how the user journey works, what revenue packages are most popular etc.

During episode 131, Alexey Gusev and Tommy Yannopoulos, Remrge’s Apptivate host, discussed that hyper casual games can be easier to manage than hardcore games. Hyper casual games have a more straightforward user flow, whereas, with hardcore titles, marketers must consider complex user patterns and behaviours.

"If it's a hyper-casual genre, within 24 hours you have plenty of events. It gets a bit tricker when we're stepping into the mid-core, hardcore genres where just the volume of the events is completely different and the user behavior patterns are way more complex in comparison to the rather straightforward casual user flow." — Alexey Gusev

App marketers can't access the granular information they need

Even with great infrastructure, there are fewer gaming insights when relying on the SkAdnetwork. App marketers cannot access granular information, so instead, the focus is on other things to compensate for it and to catch patterns. At Goodgame Studios, their number of dashboards has increased; this allows the team to observe market influence, organic lift, and uplift in general. If the user accepts the IDFA, everything needs to be married together.

Alexely noted that companies must ensure they’re acquiring users and retargeting old users.

What SKAdNetwork 4.0 means for the app measurement landscape

The biggest changes Alexey and his team have noticed with the upcoming SkAdNetwork 4.0 are the different timer windows. At the moment, there's only one postback/conversion for each install, but with the new update, there will be three postbacks.

Each postback will be up for two, seven and 35 days, which means app marketers will now be able to understand user retention to some degree and track their activity level over a more extended period. So marketers will gain important insights into retention and long-term purchasing behavior.

User privacy is important to Apple, and rightly so. However, with the new updates the privacy threshold will no longer be black and white. Instead, it will be categorized as low, medium or high tier. If an app receives a relatively low install count, Apple cannot share as much data with the advertiser because they need to protect the user's privacy. But the higher up the tiers it reaches, i.e. medium and high, the more data Apple can share with the advertiser, as the user becomes more anonymous.

Although the granularity of the data will not be as rich as the days prior to the ATT rollout, there will be more data in general for app marketers to sink their teeth into, compared to previous SKAdNetwork versions. This offers a lot of opportunities for technical integration — but the model itself has become more complicated for user position managers, which means they'll need to rely on support from others to understand it properly.

Listen to the full podcast episode with Alexey Gusev.

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Retargeting lexicon
Programmatic Advertising

The automated process of buying and selling advertising space through digital platforms.

View-Through Attribution
view-through-attribution

Refer to: Attribution Methodology

Uplift Test
uplift-test

A randomized control trial test conducted by Remerge to measure the incremental impact of one or more campaigns.

See also: Randomized Controlled Trial

Uplift Report
uplift-report

A report by Remerge showing the results of an uplift test. It presents the incremental revenue generated, on top of organic and other marketing-driven conversions. Also contains observed values such as ad spend, group sizes, amount of conversions, converters, and revenues per group, plus other metrics.

SKAdNetwork
skadnetwork

Stands for Store Kit Advertising Network. Apple’s measurement framework for tracking mobile attribution. Introduced in 2018 and widely implemented in 2020 with the iOS 14.5 update.

Segment
segment

A group of users with common attributes such as location, demographics, activity level, value or amount of purchases, and how recently they last opened a specific app.

Retention Rate
retention-rate

The share of users active in the app within certain time frames after install, reengagement, or other events.

Retargeting
retargeting

A type of marketing channel used by an app owner to engage with their existing users through other channels within the same device. Usually, the aim is to encourage users to complete a particular task e.g. completing a purchase, buying in-game currency, placing a first order. The conventional way of retargeting relies on user IDs, such as AAID and IDFA.

Reshuffle
reshuffle

Reshuffle indicates the randomization and marking of users when they were once part of a test or control group.

In incrementality measurement, reshuffling the group assignment for a specific application fights aggregated bias over time where one group doesn't see any ads while the other group is constantly exposed to them.

Reshuffling is relevant in cases where a test has been running for a long time and/or in campaigns the experience more extensive changes to the campaign setups, segmentation, or creative strategy.

Real-Time Bidding (RTB)
real-time-bidding-rtb

The process by which individual ad placements are bought and sold via programmatic auctions that happen instantaneously. With real-time bidding, ad buyers bid on an ad space, which, if the auction is won, instantly displays the buyer's ad. This lets demand-side players such as advertisers or DSPs optimize the purchase of ad placements from multiple sources.

Randomised Controlled Trial (RCT)
randomised-controlled-trial-rct

A method that randomly separates a specific population into two groups that are as similar to each other as possible, namely the test group and control group.

further reading
Queries Per Second (QPS)
queries-per-second-qps

The number of ad placements a DSP is able to process in order to determine on how to bid on them.

Publisher
publisher

Within the sphere of app marketing, a publisher is an App Developer that gets paid for placing ads within their app. For example, an advertiser wants to reach their users via App Y, so they pay App Y to display their ads.

further reading
Public Service Announcement Ad (PSA Ads)
public-service-announcement-ad-psa-ads

An incrementality testing methodology where devices in the control group are shown PSA ads, like donation drives or road safety reminders. By serving real ads, information on the devices within the control group that would have been exposed can be obtained. Unexposed devices are excluded from the measurement to reduce noise.

Probabilistic Attribution
probabilistic-attribution

Refer to: Attribution Methodology

Organic Behavior
organic-behavior

A user’s behavior not directly attributable to specific marketing efforts.

Multi-Touch Attribution
multi-touch-attribution

Refer to: Attribution Methodology

Mobile Measurement Partner (MMP)
mobile-measurement-partner-mmp

Within the sphere of app marketing, MMPs are a service provider that specializes in measuring activities that are happening within and leading to the app. An app publisher may incorporate an MMP into their app to track activity and events e.g. time spent on a certain screen, sources of incoming traffic, app opening frequencies etc.

Lifetime Value (LTV)
lifetime-value-ltv

The amount of revenue generated by the user for the App Developer during the entire duration of the relationship with the user, beginning with the app install.

Last-Click Attribution
last-click-attribution

Refer to: Attribution Methodology

Key Performance Indicator (KPI)
key-performance-indicator-kpi

The key metrics used to assess the effectiveness of an effort in achieving its objective. In programmatic advertising, the common types of performance indicators depend on the goals and nature of each campaign. These can include ROAS, cost per action, and retention rate.

Intent-to-Treat (ITT)
intent-to-treat-itt

An incrementality testing methodology where no ads from the campaign are shown to devices within the control group. Also known as a ‘holdout test’. Cost-free and easy to implement, but with a relatively high level of noise.

This method compares the behavior of all users in both groups. In the test group, this includes both exposed and unexposed users

Incrementality
incrementality

A method of measuring the impact of a specific activity, on top of organic and other activity.

Incremental Revenue (iRevenue)
incremental-revenue-irevenue

The estimated revenue caused directly by the campaign.

Formula:Revenue from test group – revenue from control group = iRevenue

Incremental Return On Ad Spend (iROAS)
incremental-return-on-ad-spend-iroas

A KPI used in calculating how cost-efficient a campaign is. This is used to evaluate the relationship between incremental revenue and the amount of money spent on the campaign. The figure is typically represented in percentage.

Formula:
Percentage: [IRevenue ÷ ad spend] × 100 = IROAS%
Ratio: IRevenue ÷ ad spend = IROAS

Incremental Cost Per Action (iCPA)
incremental-cost-per-action-icpa

A KPI used to evaluate the cost of incremental conversions.

Formula:Ad spend ÷ [test group actions – control group actions] = iCPA

Incremental Conversions
incremental-conversions

The estimated amount of conversions caused directly by the campaign.

Formula:
Test group conversions – control group conversions (scaled) = Incremental conversions

In-app Event
in-app-event

Actions made by a user within the app, such as log-in, registration, completion of onboarding, or purchases. These events can be tracked with the help of an MMP.

Impression
impression

The deployment of the ad to the ad placement. An impression might not necessarily mean that the ad has been viewed.

Identifier for advertisers (IDFA)
identifier-for-advertisers-idfa

A unique random device identifier Apple generates and assigns to every iOS device. Advertisers can use this to track user activity across apps, show them personalized ads, and attribute ad interactions.

Ghost Ads
ghost-ads

A testing methodology that shows devices in the control group an ad ran by another advertiser on the platform, therefore removing any additional cost for clicks and impressions. The control group behavior is then marked with a ‘ghost impression’, which gives the information on which control group users would have been exposed.

further reading
General Data Protection Regulation (GDPR)
general-data-protection-regulation-gdpr

A regulation under the EU (European Union) law on data protection and privacy within the EU and the EEA (European Economic Area), that grants users control over how their data is stored and used by organizations. To comply with GDPR, programmatic sellers must clearly communicate to users how their data will be stored and used. When a user gives consent to an organization to process their data, it enables targeted advertising.

Exposure Rate
exposure-rate

The percentage of devices within a test group that received at least one ad impression, versus the total number of devices within the test group targeted within a campaign during an uplift test. For example, if 900 out of 1,000 users are shown an ad, the exposure rate is 90%.

See also: Uplift Test

Deterministic Attribution
deterministic-attribution

Refer to: Attribution Methodology

Deep link
deep-link

A link that sends users directly to a specific in-app location, instead of the app marketplace. Deep links bypass the steps needed to go through to reach a conversion point, bringing the user directly to where they can perform the intended action e.g. completing a purchase, buying coins, placing an order.

Test Group
test-group

Within the sphere of app marketing, this refers to the group of devices that may be shown ads from a specific campaign in the test. The actions on these devices are then compared to the actions on the devices in the control group.

Compare with: Control Group

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Control Group
control-group

Within the sphere of app marketing, this refers to the group of devices within the target audience that are not shown ads from a specific campaign in the test. The actions on these devices are then compared to the actions on the devices in the test group.

Compare with: Test Group

further reading
Contextual targeting
contextual-targeting

A type of targeting that works with contextual signals only, such as location data (country, city, postal code), language setting, mobile operating system, device model, as well as publisher information.

California Consumer Privacy Act (CCPA)
california-consumer-privacy-act-ccpa

A bill that enhances privacy rights and consumer protection for residents of California, United States. The CCPA took effect on January 1, 2020.

The CCPA provides these rights to consumers:

- Know what personal data is being collected about them.
- Know whether their personal data is sold or disclosed, and to whom.
- Say no to the sale of personal data.
- Access their personal data.
- Request a business to delete any personal information that was collected from that consumer.
- Equal service and price, even if they exercise their privacy rights.

Attribution Window
attribution-window

A specific time frame that is taken into consideration when determining the source of a user’s action.

Attribution Provider (AP)
attribution-provider-ap

A role played by an MMP to credit the in-app activity of users to the correct media sources.

Attribution Methodology
attribution-methodology

Refers to the process of identifying which conversions belong to which preceding click or impression. Common attribution methodologies include:

  • Click-Through Attribution - Determines the source of a conversion based on the user’s click activity.

  • View-Through Attribution - Determines the source of a conversion based on the ad impression delivered to the user.

  • Deterministic Attribution - A model that establishes the origin of a user’s conversion from a specific click or impression, based on unique device IDs.

  • Probabilistic Attribution - A model that establishes the likelihood of a user’s conversion originating from a specific click or impression, based on the data logged on both occasions, such as device language, timezone, IP address, and OS version.

  • Last-Touch Attribution - A model that establishes a match between the action taken by a user (e.g. app open, purchase) and its corresponding ad click or impression. When a user converts from an ad, the DSP that delivered the respective ad is given full credit for that conversion event.

  • Multi-Touch Attribution - Also known as multi-channel attribution. A model determines the value of every touchpoint on the way to a conversion. Rather than giving full credit to one ad, multi-touch attribution divides the credit among all advertising channels that the user has interacted with, leading to the conversion.
Attribution
attribution

A method of identifying the touchpoints a user has encountered within a specified period before making a conversion.

App Tracking Transparency (ATT)
app-tracking-transparency-att

The privacy framework from Apple that, among other things, manages the process of obtaining user consent before accessing their Identifier for Advertiser (IDFA).

App Monetization
app-monetization

The strategy a publisher employs to earn money from their app. This can be done through in-app advertisements, paid membership, and charging for premium features or an ad-free experience, among others. For example, some gaming apps are free to download and play, but users may need to pay in order to progress to the next level quickly.

Android Advertising identifier (AAID)
android-advertising-identifier-aaid

Also known as Google Advertising Identifier. A unique device identifier that Android generates and assigns to every device. Advertisers can use this to track user activity across apps, show them personalized ads, and attribute ad interactions.

Advertisers
advertisers

The advertiser is a person or legal entity focusing on generating sales and leads through serving ads that convey the right message to the right audience at the right time.

In mobile advertising, the advertiser is on the client-side and is the one interested in promoting an app.

Causal Impact Analysis
causal-impact-analysis

A measurement framework developed by Google that works without device IDs. It measures the incremental uplift of one or more conversion events, removing the influence of other campaigns and organic conversions. Used to assess the effect of ID-less campaigns.

Similar to measuring the effect TV ads have, the principle is based on running campaigns on identifiable sub-markets (test group), while leaving other sub-markets unexposed (control group).

Ghost Bids
ghost-bids

An incrementality testing methodology based on Ghost Ads, adapted for retargeting campaigns. The difference is that it removes all devices that are not seen on ad exchanges, or that would not be bid on, from both test and control groups, to reduce noise. A bid is placed as usual for the test group, while the control group is tracked with ‘ghost bids’ (bids that could have been placed, but weren’t in the end).

Return on Advertising Spend (ROAS)
return-on-advertising-spend-roas

A KPI that measures the relationship between the revenue generated by specific advertising efforts and the money spent on them.

Formula

Percentage: [Revenue ÷ ad spend] × 100 = ROAS%

Ratio: Revenue ÷ ad spend = ROAS

See also: Incremental Return On Ad Spend

Supply-Side Platform (SSP)
supply-side-platform-ssp

A company that works with publishers to sell ad inventory across ad networks.

Demand-Side Platform (DSP)
demand-side-platform-dsp

A company that works with advertisers to purchase ad inventory across ad networks. Their platforms are built to identify a desired ad space and place bids on it.

Compare with: Supply-Side Platform

Open RTB
open-rtb

A digital marketplace where ad inventory from multiple publishers are available for advertisers to bid on in real time.

See also: Real-Time Bidding

Self-Attributing Network
self-attributing-network

An ad network like Meta, Snap, and Twitter, that attributes its traffic internally, without the involvement of third-party MMPs.

Variable Bidding
variable-bidding

The dynamic adjustment of bid prices based on a user's in-app behavioral patterns, contextual information, time of day, and ad placement performance.

Dynamic Product Ad (DPA)
dynamic-product-ad-dpa

Also known as a dynamic ad. It is dynamically assembled based on the user’s behavior and information sourced from a feed. This type of ad delivers a tailored experience for individual users.

Real-Time Audience Segmentation
real-time-audience-segmentation

The division of an audience into distinct segments based on real-time events, thus enabling targeted advertising and alignment with a user's behavioral patterns and preferences.

User Acquisition (UA)
user-acquisition-ua

A mobile marketing effort used to attract new users to an app. Paid UA may refer to ads shown in mobile ad networks or social media channels, while non-paid UA involves app store optimization and promotion on the advertiser’s own channels.

Programmatic Advertising
programmatic-advertising

The automated process of buying and selling advertising space through digital platforms.

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